Use of QTLs in Developing Abiotic Stress Tolerance in Rice

CHAPTER

USE OF QTLS IN DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

43

Chandra Prakash1, Amitha Mithra Sevanthi1 and P.S. Shanmugavadivel2 1

ICAR-National Research Centre on Plant Biotechnology, PUSA Campus, IARI, New Delhi, Delhi, India 2Division of Plant Biotechnology, ICAR-Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

43.1 INTRODUCTION The discovery of restriction enzymes by Luria and Human (1952) made it possible to cut and paste DNA. This further facilitated a possible way of defining a large number of polymorphic marker loci known as restriction fragment length polymorphism (RFLP; Botstein et al., 1980). This allowed, for the first time in history, the direct association of sequences of DNA with its effect on a phenotype (Martinville et al., 1982). Utilization of RFLP based linkage maps (Lander and Botstein, 1986, 1987, 1989) to localize genes responsible for variation in phenotypes laid the foundation for modern day linkage analysis and genome-wide association studies. This allowed, for the first time in history, the direct association of sequences of DNA with its effect on a phenotype by RFLP mapping of Huntington’s disease in humans (Gusella et al., 1983). With the evolution of markers from the first generation like RFLP, single-strand conformation polymorphism (SSCP; Orita et al., 1989), sequence tagged sites (STS; Olson et al., 1989), and amplified fragment length polymorphism (AFLP; Vos et al., 1995), etc., to the new generation of markers single nucleotide polymorphism (SNP; Wang et al., 1998), sequence-related amplified polymorphism (SRAP; Li and Quiros, 2001), Insertions-Deletions (InDels; Mills et al., 2006), and intron-retrotransposon amplified polymorphism (IRAP; Kalendar and Schulman, 2006), coupled with an advancement in the computing capacity of computers, it became possible to construct high density genetic linkage maps in almost every species. Thus, geneticists have added a new tool to their armory with which they could tag phenotypes easily. Genetic variation in Nature often takes the form of a quantitative trait (QT), with an approximately normal distribution, rather than discrete phenotypes which we are familiar with, based on our understanding of Mendelian genetic principles. The genetic variation underlying quantitative phenotypes, such as plant yield and height, pigmentation, grain number, abiotic stress tolerance, etc., result from the segregation of numerous QT loci, abbreviated as QTLs (Mackay 2001). Since Mendelian genetic ratios are disguised in QTs, they need to be dealt with differently from simply inherited traits and the mapping of QTs requires different methodologies. To understand QTL mapping, it is important to understand the mean [effect and breeding values (BVs); Box 43.1] and

Advances in Rice Research for Abiotic Stress Tolerance. DOI: https://doi.org/10.1016/B978-0-12-814332-2.00043-5 Copyright © 2019 Elsevier Inc. All rights reserved.

869

870

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

BOX 43.1 BASIC TERMINOLOGIES IN QTL MAPPING Additive effect of a QTL refers to change in mean value of the phenotype upon replacement of QTL allele from one parent by the other parent. Similarly, dominance effect refers to the effect of QTL allele when the marker alleles are in heterozygous condition. Phenotypic variance explained: Each QTL explains a portion of the total variation of the phenotype and this proportion is the popular R2 value. Heritability (H) It is proportional of phenotypic variance (Vp) which is explained by genetic variance (Vg) [H 5 Vg/Vp]. Logarithm of Odds is a log10 ratio of likelihood. In linkage analysis, this represents the likelihood of linked loci to no linkage. In QTL analysis, it represents the likelihood of the presence of QTL to no QTL. Confidence interval (CI) of QTL location can be calculated using the formula given by Darvasi and Soller (1997); where, CI 5 3000/mNd2, m 5 relative number of informative meiosis, N 5 population size, and d 5 allele substitution effect. It can also be calculated by 530/Nv formula where v is the proportion of variance explained. BV is the sum of average effects of its alleles. Average effect of alleles (α) can be defined as “the mean deviation from the population mean of individuals which received that allele from one parent, the other allele coming at random from the population.”

variance components of populations besides the fundamentals of mapping populations, and the genetic basis and properties of DNA (molecular) markers. Rice is a representative model of cereal food crops and is the staple food for a large portion of the world’s population, especially, in South East Asia. Rice-growing areas span the tropics, subtropics, semi-arid tropics, and temperate regions. However, it is susceptible to various abiotic stresses like drought, salinity, and heat stress which hamper its grain yield (GY) potential and stability across various ecosystems (Munns and Tester, 2008). Domestication and breeding for high yield caused the genetic erosion of alleles conferring abiotic stress tolerance. Therefore, efforts are now being made to reconstitute the allelic diversity for abiotic stress tolerance in modern day high yielding varieties from locally adapted cultivars and germplasm. Being complex in inheritance, it is important to genetically dissect QTLs for various abiotic stress tolerances between the high yielding elite backgrounds (IR64, IR72, Swarna, Way Rarem, Nipponbare, and IR29) and the locally adapted and stress tolerant cultivars (Nagina 22, Apo, DK 151, Pokkali, Nona Bokra, CSR27, Bala, and cultivar 996). Many favorable alleles for abiotic stress tolerance have been contributed by stress sensitive genotypes/parents (Lanceras et al., 2004; Bernier et al., 2007; Sandhu et al., 2014; 2015; Tiwari et al., 2016), which indicates the importance of genetic background of a genotype upon its performance under stress. The use of QTLS in abiotic stress tolerance is also a matter of robust screening and evaluation protocols, gene 3 genetic background interaction, and gene 3 environment interaction. Most of the QTLs perform well under managed stress conditions but fail in field conditions and, therefore, MAS for abiotic stress tolerance is not straight forward. The amalgamation of whole genome expression data, QTL information, and meta-QTL analysis has emerged as a competent tool for narrowing down the search for candidate genes for abiotic stress tolerance (Jansen and Nap, 2001; Yano et al., 2012; Sandhu et al., 2017). There are many success stories of introgression of QTLs for abiotic stress tolerance, and many varieties are in the advanced field trails stage (Singh et al., 2016) for tolerance to drought, salinity, and heat separately or in combination.

43.2 QTL MAPPING

871

43.2 QTL MAPPING QTL is defined as “a region of the genome that is associated with an effect on a quantitative trait” (Geldermann, 1975). QTL analysis is a multivariate statistical method which associates quantitative phenotype data with qualitative genotype (marker) data with the aim of explaining the genetic basis of variation in complex traits (Falconer and Mackay, 1996; Kearsey, 1998; Lynch and Walsh, 1998). QTL analysis can tell us a lot about genetic variation and answer important questions on the number of genes underlying a trait, the effect of each gene on the phenotype, and whether these genes operate independently or show epistasis. Since the number of QTLs is always much lower than the number of markers, QTL mapping can be viewed as an issue of model selection (Broman and Speed, 2002; Sillanpa¨a¨ and Corander, 2002). In essence, QTL mapping is a simple procedure where the phenotype data of the mapping population, derived from contrasting parents for the trait of interest, is analyzed along with the genome-wide polymorphic marker data of the population so as to identify any association, if any exist, between these two. Since mapping populations are nonstructured, wherein all polymorphic marker alleles occur in equal frequencies, such associations are possible only when there is linkage between the markers and the QTL. A simple procedure for QTL mapping is given in Fig. 43.1. This knowledge of associating a simply inherited trait (like molecular marker) with a QT has been known since as early as 1923 by the classical experiment on seed weight in Phaseolus vulgaris and its association with pigmentation (Sax, 1923). The only limitation to extending this logic was the lack of availability of such qualitative traits which are linked to QTs. This observation was

FIGURE 43.1 A schematic representation of the steps involved in QTL mapping. Contrasting parents are selected to produce a mapping population. Polymorphic markers between both the parents are used for genotyping of the mapping population and then a genetic linkage map is constructed. Phenotyping of the mapping population is performed for the traits associated with abiotic stress tolerance. In the last step, QTL mapping is performed using various software. QTL, Quantitative trait loci.

872

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

well summarized as early as 1961 by Thoday, a Drosophila geneticist, as “The main practical limitation of this technique seems to be the availability of suitable markers, and the time that can be given to the considerable work involved” (Thoday, 1961).

43.2.1 PREREQUISITES FOR QTL MAPPING—MAPPING POPULATIONS AND DNA MARKERS Mapping populations are non-structured populations developed by crossing two or more parents, contrasting in the trait of interest, in a definite fashion and maintaining the entire progeny without exercising any selection. Objective, time, and budget of the study will determine the choice of the mapping population for QTL mapping. The most frequently used mapping populations for QTL mapping are F2 derived F3 (F2:3), backcross populations (BC), recombinant inbred lines (RILs) and doubled haploids (DHs). F2:3 and BC are appropriate for measuring dominance effect, additive effect, and all types of interactions. However, RILs and DH cannot measure dominance effects and interactions involving dominance effects since they do not have harbor heterozygosity. All populations can measure additive effects and additive 3 additive interactions. F2 populations can be constructed in less time and, therefore, are ideal for the initial mapping of markers, oligogenes, and heterotic QTLs. In the BC populations, QTL mapping and introgression of QTL into elite varieties can be performed simultaneously. But, they can’t be replicated in trial, are labor intensive, and capture recombination from only one parent. Since QTs are highly influenced by environment, RIL and DH populations are ideal for QTL mapping as they are immortal and can be replicated within and across seasons and locations without loss of genetic structure. Mapping populations developed by crossing two parents only capture limited genetic variability for the trait of interest, do not generally allow high resolution mapping owing to limited genetic recombination, and the estimates of effects are not robust, especially in the ephemeral populations (BC and F2:3). Hence, mapping populations involving multiple parents such as Nested Association mapping populations and Multiparent advanced generation intercross lines have become popular in maize, rice, wheat, and barley as they can identify more QTLs for the trait of interest, as they capture more variability, measure the QTL effects including epistasis precisely, and can possibly allow high resolution mapping.

43.2.2 MOLECULAR MARKERS Molecular markers can be defined as polymorphic DNA sequences having a stable location in the genome following Mendelian inheritance. DNA polymorphism is the variation in two or more individuals in the nucleotide sequence at the corresponding positions on homologous chromosomes which follows Mendelian inheritance. DNA markers are amenable to non-destructive assay and are stable. They are independent of environment, stage, or tissue for detection. They show neither pleiotropy nor epistasis. simple sequence repeat (SSR) and SNP are two widely used markers in QTL mapping for abiotic stress due to their abundance, reproducibility, and polymorphic nature. Microsatellites or SSRs are simple sequence tandem repeats which are between 1 and 6 nucleotides long and are repeated “n” number of times. Microsatellites are multiple-allelic, locus specific, co-dominant markers distributed in both genic and non-genic regions. Since the repeats evolve

43.3 METHODS AND SOFTWARE FOR QTL DETECTION

873

faster than other nucleotides, due to slippage and unequal crossing over, they are highly polymorphic. SSRs are also known by other names such as simple sequence length polymorphisms and sequence tagged microsatellite site. Owing to their highly polymorphic nature, co-dominance, abundance, and ease in genotyping, even in the current era of SNPS, microsatellite markers are predominantly used. SNPs are the most preferred markers for QTL mapping at present due to their high frequency, low mutation rates, and high-throughput nature. SNP is an individual nucleotide base difference between homologous DNA sequences from two or more genotypes. SNPs are the most abundant and ultimate molecular marker system because they make up about 90% of all genetic variation. Any SNP variation that occurs in at least 1%
5% (depending upon the population size) of the population is considered as a valid marker. SNPs are meaningful only when their position is clearly defined with respect to a reference genotype. SNPs are mostly biallelic for mainly two reasons: (1) the probability of having triallelic or tetra allelic SNPs is very low (10212 and 10218, respectively, considering that the spontaneous mutation rate is 1026) and (2) the frequency of transitions is much higher than that of transversions. SNPs can be genotyped either by using hybridization or PCR based methods. The major SNP detection or genotyping techniques include resequencing, comparison of available genomic sequences, denaturing gradient gel electrophoresis, single cell gel electrophoresis, SSCP, RFLP, and cleaved amplified length polymorphism (CAPS), etc.

43.3 METHODS AND SOFTWARE FOR QTL DETECTION QTL mapping is a statistical method for mapping and estimation of genetic effects of a locus on the genome responsible for a QT. It assumes that if a genomic region/locus (QTL) is influencing a trait expression, then the marker-trait associations could be established in a population segregating for the QTL of interest. Furthermore, association strength is proportional to the linkage between the marker and the trait as well as the effect of the QTLs. Different QTL mapping methods are described in Table 43.1 with their principle, advantages and disadvantages. Most of the prevalent QTL mapping software (Table 43.2) such as MapMaker/QTL (Lander and Botstein, 1987), Map Manager (Manly et al., 2001), R/qtl (Broman et al., 2003), and Table 43.1 Different QTL Mapping Methods, Their Principle, Merits, and Demerits Method

Principle

Merits

Demerits

References

SMA

The null hypothesis is genotypic classes do not differ in phenotype for a given molecular marker Statistical analyses, including t-tests, ANOVA, regression, maximum likelihood estimations, and log likelihood ratios are performed

Easy algorithm and less computer intensive

Cannot tell whether markers are associated with one or more QTLs

Soller et al. (1976)

QTLs effects are underestimated

(Continued)

874

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Table 43.1 Different QTL Mapping Methods, Their Principle, Merits, and Demerits Continued Method

Principle

Merits

Demerits

References

IM

If the maximum likelihood profile at a region exceeds a predefined critical threshold, a QTL is declared

QTL position can be estimated

Lander and Botstein (1989)

CIM

Covariates significantly associated with the trait are selected on the basis of forward and backward stepwise regression analysis Interval mapping is used to fit a linear model at every position for the genome scan Uses all the marker information to build the linear regression model of CIM, including marker effect at the current testing position. Unlike CIM, separate estimation of QTL and background effects Uses several selection methods like forward and backward selection methods to search for the best genetic model

Improved QTL detection and estimation of effects than with IM

When there are two or more QTLs located on a chromosome, the mapping of QTLs can be seriously biased, and QTLs can be mapped to wrong positions Cannot detect epistatic QTLs Cofactors are chosen arbitrarily

Faster than CIM Detects dominance and epistasis

Limited to bi-parental mapping population only

Li et al. (2007)

Estimates interacting QTLs

Computationally intensive Results may vary according to model selection methods Computationally intensive

Kao et al. (1999)

Time consuming Difficulties in choosing a prior distribution

Satagopan et al. (1996); Banerjee et al. (2008)

ICIM

MIM

MQM

Bayesian mapping

Two step QTL mapping procedure in the first Model selection phase, important marker cofactors are selected by multiple regressions and backward elimination. Then, in the second phase, interval mapping is done by maximum likelihood or restricted maximum likelihood methods based on models selected from step one It treats the number of QTLs as a random variable and uses reversible-jump MCMC procedure for specific modeling

Improved handling of missing data Can handle large genomics datasets Higher statistical power

Efficient genomewide mapping strategy for correlated traits

Jansen and Stam (1994), Zeng (1994)

Arends et al. (2010)

QTL, Quantitative trait loci; SMA, Single marker analysis; IM, Interval mapping; CIM, Composite interval mapping; ICIM, Inclusive composite interval mapping; MIM, Multiple-interval mapping; MQM, Multiple QTL mapping; MCMC, Markov Chain Monte Carlo.

43.3 METHODS AND SOFTWARE FOR QTL DETECTION

875

Table 43.2 List of Software for QTL Mapping Name of Software EBEN FastQTL FlexQTL HpQTL Ici Mapping lme4qtl

OS

URL

Reference

Unix/Linux, Mac OS, Windows Unix/Linux, Mac OS Unix/Linux

https://cran.r-project.org/web/packages/ EBEN/index.html http://fastqtl.sourceforge.net/ https://www.wur.nl/en/show/FlexQTL.htm

Huang et al. (2015)

Unix/Linux, Mac OS, Windows Windows

https://github.com/simecek/HPQTL

Unix/Linux, Mac OS, Windows Mac OS, Windows

http://www.isbreeding.net/software/? type 5 detail&id 5 18 https://github.com/variani/lme4qtl

Map Manager QTX MapMaker/QTL

Windows

MapQTL

Windows

MultiQTL Qgene

Unix/Linux Unix/Linux

QTL Express QTLBIM

Web based Unix/Linux, Mac OS, Windows Unix/Linux, Mac OS, Windows, Web Unix/Linux

http://qtl.cap.ed.ac.uk/ http://www.ssg.uab.edu/qtlbim/index.jsp

Unix/Linux, Windows Unix/Linux, Windows Unix/Linux, Windows Unix/Linux,

Mac OS,

http://www.rqtl.org/

Mac OS,

https://github.com/dg13/rasqual

Mac OS,

https://github.com/ugcd/solarius

Windows

http://statgen.ncsu.edu/qtlcart/index.php

QTL Network QTLseqr R/qtl RASQUAL solarius WinQTL Cart

http://mapmgr.roswellpark.org/mmQTX. html http://gaow.github.io/genetic-analysissoftware/m/mapmakerqtl/ https://www.kyazma.nl/index.php/mc. MapQTL http://www.multiqtl.com/ http://www.qgene.org/qgene/index.php

http://ibi.zju.edu.cn/software/qtlnetwork/ webservise/ https://github.com/bmansfeld/QTLseqr

Ongen et al. (2016) Hern´andez Mora et al. (2017) Sun et al. (2017) Li et al. (2007) Ziyatdinov et al. (2018) Manly et al. (2001)


Joehanes and Nelson (2008) Seaton et al. (2002) Yandell et al. (2007) Yang et al. (2008) Mansfeld and Grumet (2018) Broman et al. (2003) Kumasaka et al. (2015) Ziyatdinov et al. (2016) Wang et al. (2006)

WinQTL Cart (Wang et al., 2006) were developed and implemented for single marker analysis (Soller et al., 1976), interval mapping (Haley and Knott, 1992; Lander and Botstein, 1989), Bayesian mapping (Satagopan et al., 1996), composite interval mapping (Zeng, 1994), multipleinterval mapping (Kao et al., 1999), inclusive composite interval mapping (Li et al., 2015), or multiple QTL mapping (Arends et al., 2010).

876

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Most of the QTL mapping software are open source and could be operated from Unix/Linux, Mac OS, or Windows. Some QTL mapping tools (QTL Network and QTL Express) are also available online where the user has to upload data and can perform QTL analysis. A freely available package R/qtl with mqm method, is quite suitable for high-throughput QTL analysis and eQTL analysis of omics datasets generated by high-throughput technologies. It also gives QTL interaction networks and many other outputs which are very useful for omics data representation. Since each algorithm has some pros and cons, it is advisable to use more than one tool for better QTL analysis. In the omics era, QTL mapping has become more computationally intensive. Most of the old QTL mapping software cannot handle high-throughput data and are becoming obsolete due to the lack of features which support and represent omics data. QTL mapping has grown from simple phenotype-based mapping, to expression microarray and RNA-seq data (eQTL), metabolome (mQTL), and proteome (pQTL) based mapping. Thus, the quantum of traditional phenotype data has grown multi-fold. In the past, the number of polymorphic markers in a mapping population was limited to a few hundred markers, but now due to high-throughput technologies such as sequencing and microarray-based genotyping, the number of informative markers has also increased manifold. Most of the software developed earlier are unable to handle such big data and are slowly becoming obsolete. This also calls for the development of new algorithms and model selections for declaring significant QTLs.

43.4 PRACTICAL CONSIDERATIONS IN QTL MAPPING FOR ABIOTIC STRESS TOLERANCE Molecular mapping of QTLs for abiotic stress tolerance poses many problems, and the results from mapping studies are affected by a variety of factors including the selection of traits, choice of parents, screening or phenotyping methods, stress imposition, and successful introgression into elite varieties without linkage drag. GY under stress is the primary trait in any breeding program as GY is a QT, the efficiency of selection for GY was found to be low (Rosielle and Hamblin, 1981; Blum, 1988), but IRRI has found moderate to high heritability of GY under stress conditions (Bernier et al., 2007; Venuprasad et al., 2007; Kumar et al., 2008) suggesting that direct selection for GY under stress is a practical approach (Kumar et al., 2008; Venuprasad et al., 2007). The identification of QTLs for abiotic stress susceptible and tolerance yield indices by evaluating the mapping population in both irrigated (well-managed) and stress situations can enable breeders to select genotypes with less yield penalty under non-stress conditions as compared to currently cultivated varieties in drought prone areas (Raman et al., 2012; Shanmugavadivel et al., 2017). Sometimes secondary trait-based selection is useful for the breeders as they provide some additional information and make the selection of donor parents easy. There are some pre-requisites to the selection of secondary traits such as they should show a high correlation with yield, high heritability, less affected by environment, and must also perform well under non-stress conditions (Fischer et al., 2012). However, the accumulation of favorable secondary traits in an elite variety is a difficult task and as such it has limited success. Defining the target environment for abiotic stress tolerant rice varieties development is also very important due to G 3 E interactions. A QTL performing well under one environment may behave

43.5 QTLS DEPLOYED IN ABIOTIC STRESS TOLERANCE IN RICE

877

differently in another environment. Care must be taken while introgressing different alleles and their cumulative effects in the elite background by repeated evaluation under well-managed stress conditions.

43.5 QTLS DEPLOYED IN ABIOTIC STRESS TOLERANCE IN RICE 43.5.1 RICE PRODUCTIVITY UNDER DROUGHT STRESS Agriculture under rainfed conditions is the most widespread cultivation practice around the world, and its productivity is only 50% of its potential yield under standard cropping practices. Water scarcity is a serious challenge to rice production; however, rice uses about 3500 L of water throughout its life period to produce 1 kg of rice (World Water Development Report, 2012). Furthermore, unpredictable, insufficient, and uneven rainfall is adding complications to the situation. Drought responses showed by roots, shoots, and leaves depend on the stress timing in relation to the stage of plant growth (e.g., early or seedling, vegetative, reproductive, or terminal drought), severity level of drought (mild, moderate, or severe), edaphic properties, and the environment of the habitat (Fukai and Cooper, 1995). Drought stress is a major cause of yield instability in rice production across diverse crop ecosystems and may even lead to complete crop failure if it coincides with the reproductive stage (Pantuwan et al., 2002; Venuprasad et al., 2007; Khan, 2012; Xangsayasane et al., 2014), and therefore it is important to genetically enhance rice plants for drought stress tolerance without compromising performance under non-stress conditions.

43.5.2 QTLS FOR DROUGHT STRESS TOLERANCE IN RICE Drought-tolerant varieties produce higher yield as compared to other varieties under drought conditions in the target population of environments (TPE), and also respond well under favorable conditions. Drought is difficult to define because it is the interaction of various moisture uptake and loss sources like precipitation, evapotranspiration, irradiation, soil properties, etc., with the various mechanisms of drought resistance in plants like drought escape, drought avoidance (DA), and drought tolerance (DT) (Price et al., 2002a). In addition, yield itself is a complex trait governed by many component traits, and together with drought conditions becomes extremely challenging. The selection of the traits for QTL mapping under selection environments is very critical. The use of secondary traits together with yield helps with improving the selection response. Roots are primary sensors of stress, and therefore, serve as a potent target for improvement. In Bala 3 Azucena population of 140 RILs Price et al. (2002b) mapped 24 root QTL regions. Under both well-watered and water-limited conditions a QTL on chromosome 9 was found to be associated with root/shoot ratio, root thickness, deep root weight, and maximum root length. Babu et al. (2003) associated secondary traits with field performance in rice plants under drought by mapping QTLs for plant stress indicators, phenology, and yield. They have found root traits on chromosome 4 that have a positive correlation, and thereby pleiotropic effect, with yield and its component traits under drought. Lanceras et al. (2004) identified QTL regions for yield and its component traits, and other agronomic traits in 154 DH lines derived from parents CT9993 and IR6226 using SSR, RFLP, and AFLP markers. They have identified a total of 77 QTLs for GY, biological yield,

878

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

harvest index, total spikelet number, percent spikelet sterility, panicle number, and plant height explaining a 7.5%
55.7% phenotypic variation. Only QTLs on chromosome 3, 4, and 8 for biological yield and harvest index were highly correlated with yield under reproductive-stage drought conditions. In 2006, Yue et al. resolved genetic regions for DT and DA at the reproductive stage of rice plants. They have investigated 21 traits related to DT and DA like yield, its component traits, harvest index (HI), leaf drying, leaf rolling, and different root system traits. A weak association between relative yield traits and potential yield, plant size, and root traits coupled with a little overlap of their QTL regions, indicated unique regions for DT and DA in rice. The fact that alterations in root system architecture improve DA in rice plants was proved by Uga et al. (2013). They have backcrossed earlier identified Dro1 (controls root growth angle) on chromosome 9 (Uga et al., 2011) in drought-susceptible IR64 background, and found that it enabled IR64 Near isogenic lines (NILs) (Dro1 backcrossed) to avoid drought by deep rooting. The first major and consistent QTLs for GY under severe drought stress were reported by IRRI (Bernier et al., 2007). Random 436 F3 derived lines from Vandana and Way Rarem was QTL mapped under reproductive-stage drought stress and a major QTL (qtl12.1/qDTY12.1) on chromosome 12 between SSR markers RM28048 and RM511 was found. This QTL was associated with increased HI, higher biomass and plant height, and a reduced number of days to flowering, explaining 51% of the genetic variance for GY. Later, to estimate the effect of qtl12.1 under diverse TPE, Bernier et al. (2009) evaluated qtl12.1 over 21 trials in the Philippines and India, and reported its consistent effect on GY under drought conditions across environments. However, consistency of major effect yield QTLs in different genetic backgrounds is also needed. Vikram et al. (2011) evaluated three populations N22/IR64, N22/MTU1010, and N22/Swarna and mapped a major consistent GY QTL, qDTY1.1 across three populations on chromosome 1, suitable for marker-assisted breeding (MAB). qDTY1.1 was also detected using the bulk seggregant analysis (BSA) approach in the genetic background of Swarna and IR64 when crossed with Dhagaddeshi as a donor parent and explained 32% and 9.3% of the phenotypic variation respectively for GY under drought stress (Ghimire et al., 2012). In addition, qDTY1.1 and the locus for plant height (sd1) were found linked in Vandana/IR64 populations (Venuprasad et al., 2012a), and therefore, in large segregating population recombinant alleles having unlinked qDTY1.1 and sd1 may produce drought-tolerant plants with reduced height (Vikram et al., 2016). Earlier qDTY2.1 and qDTY3.1 were mapped on chromosome 2 and 3 in Apo/Swarna RIL population using BSA and were shown to be strongly associated with GY under lowland drought stress (Venuprasad et al., 2009). qDTY2.1 and qDTY3.1 explained 13%
16% and 31% of the phenotypic variance respectively and were first report of a large effect QTL under both lowland and aerobic environments. These QTLs also influence other traits such as DTF and plant height. In 2012, Dixit et al. fine mapped earlier identified QTLs, namely, qDTY2.1, qDTY2.2, qDTY9.1, and qDTY12.1 (Bernier et al., 2007; Venuprasad et al., 2009; Swamy et al., 2011; Mishra et al., 2013) for GY under drought using different backcross derived populations made by crossing six parents (Apo, Swarna, Aday sel, IR64, Way Rarem, and Vandana) under upland, lowland, stress, and non-stress environments. Another major QTL “qDTY6.1” (Venuprasad et al., 2012b) explaining 40%
66% of the genetic variation for GY in aerobic conditions was mapped on chromosome 6 in Apo/Swarna, Apo/IR72, and Vandana/IR72 genetic backgrounds. This QTL enhanced performance of drought-susceptible Swarna and IR72 in aerobic conditions. Furthermore, this was the first report of a major QTL that enhances both yield and yield potential under aerobic conditions. Nevertheless, this QTL had no effect on lowland drought stress conditions.

43.5 QTLS DEPLOYED IN ABIOTIC STRESS TOLERANCE IN RICE

879

A mapping population made by crossing Dular (drought tolerant) and IR 64
21 (drought-sensitive) was used to identify QTLs for drought stress (Catolos et al., 2017). QTL mapping identified three QTLs for GY (qDTY1.1, qDTY1.3, and qDTY8.1) under both non-stress and reproductivestage drought stress conditions, and two QTLs for root traits (qRT9.1, qRT5.1). Chromosome 1 region harboring qDTY1.1 was found to be associated with yield, agronomic traits, and root related traits. A GY advantage of 24.1%
108.9% and 3.0%
22.7% was estimated in the lines with QTLs over the lines without QTLs under reproductive-stage drought stress. All these developments in QTL mapping, trait selection under TPE, and standardization of screening protocols paved the foundation for their use in marker-assisted breeding programs for the development of droughttolerant varieties.

43.5.3 QTLS DEPLOYED IN DEVELOPING DROUGHT TOLERANT RICE VARIETIES The improvement of GY with consistent gain quality was attempted at IRRI through MAB. Three GY QTLs under drought stress namely qDTY2.2, qDTY3.1, and qDTY12.1 were introgressed into high quality Malaysian rice cultivar MRQ74 by MAB. The aim of the experiment was to enhance the performance of pyramided MRQ74 lines under drought stress and study the interaction between these three QTL combinations (Shamsudin et al., 2016). They have identified five promising pyramided lines (PLs) with yield advantage of more than 1 t ha21 under RS as compared to MRQ74. Furthermore, PLs with qDTY2.2 and qDTY12.1 showed synergistic interaction compared to other QTL combinations. An Indian project in collaboration with IRRI: “From QTL to variety: markerassisted breeding of abiotic stress tolerant rice varieties with major QTLs for drought, submergence and salt tolerance” has introgressed seven consistent QTLs for GY under drought (i.e., qDTY1.1, qDTY2.1, qDTY2.2, qDTY3.1, qDTY3.2, qDTY9.1, and qDTY12.1) into high yielding, submergence-tolerant elite backgrounds of Swarna-Sub1, Samba Mahsuri-Sub1, and IR64-Sub1 (Singh et al., 2016). The aim of this project is to develop drought-tolerant varieties suited for rainfed eastern, north-eastern, and southern India. Phenotypic evaluation of BC lines is under progress and commercial release is expected soon. In a study using BSA, Yadaw et al. (2013) have identified a GY QTL qDTY3.2 under lowland drought stress conditions. NILs of a droughtsusceptible variety Sabitri was made by introgressing Sabitri with qDTY3.2 (IR77298-5-6-18 donor parent) and qDTY12.1 (IR74371-46-1-1 donor parent) using Marker-assisted backcross breeding (Dixit et al., 2017). Sabitri NILs having positive alleles for both QTLs with Sabitri grain type were identified. Field evaluation of drought-tolerant Sabitri NILs also showed early flowering, reduced plant height, and high GY as compared to Sabitri under drought stress conditions.

43.5.4 SALINITY STRESS Rice is highly sensitive to salt stress with a threshold of 3 dS m21 while the seedling and reproductive stages are highly sensitive but the germination, active tillering stages are relatively tolerant to salinity stress (Thitisaksakul et al., 2015). Over 800 million hectares of land including 20% of the irrigated land globally is affected by salinity (Munns, 2005). Salinity is described as the presence of elevated levels of different salts such as sodium chloride, magnesium and calcium sulfates, and bicarbonates in soil and water. Rice responds differently to salinity depending on developmental stage, type and concentration of salt, duration of exposure, water regime, soil pH, humidity, solar

880

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

radiation, and temperature (Das et al., 2015). The addition of as little as 50 mMNaCl in ricegrowing soil can reduce yield of rice significantly. Salt stress generally reduces seed germination, decreases growth and survival of seedlings, damages the structure of chloroplasts, reduces photosynthesis, and inhibits seed set and GY (Lutts et al., 1995; Asch et al., 2000; Moradi and Ismail, 2007; Yamane et al., 2008; Hakim et al., 2010). Salinity causes osmotic and ionic stresses in plants. Osmotic stress occurs after the concentration of salts around the root zone of the plant increase beyond a threshold tolerance level, while ionic stress occurs when the concentration of salt in old leaves reaches a toxic level due to the influx of large amounts of Na1 ions into the plant, resulting in increased Na1 concentrations in the vacuoles and cytoplasm leading to the interruption of metabolic processes and cell death (Munns and Tester, 2008). Rice has evolved various mechanisms to manage salinity stress including biosynthesis and accumulation of osmolytes, ion homeostasis, compartmentation and antioxidant ROS detoxification, and programmed cell death (Hoang et al., 2016). In general, indica rice varieties are more tolerant to salt stress than japonica varieties since they are better Na1 excluders, maintain a low Na1/K1 ratio and absorb higher K1 (Lee et al., 2003). Oryza coarctata, an Asian wild rice species occurring mostly in the coastal areas of India is highly resistant to salt stress because of their survival ability in coastal environments and special unicellular hairs on the adaxial surface of their leaves called trichomes which efficiently maintain a low concentration of toxic salts in the plant tissue (Bal and Dutt, 1986).

43.5.5 QTLS FOR SALT STRESS TOLERANCE IN RICE Salinity tolerance at the seedling and reproductive stages is a complex trait both genetically and physiologically and is regulated by a different set of genes under QTLs (Moradi et al., 2003). Salinity tolerance at the seedling stage and flowering/reproductive stage is independent of each other as observed in CN499-160-13-6 (Mohammadi-Nejada et al., 2010). Flowers and Yeo (1981) found that Na1/K1 ratio correlates with the growth of rice seedlings and GY under saline conditions. Saltol QTL is a major salt-tolerant QTL identified so far and used extensively to improve superior rice cultivars worldwide. Saltol was initially identified by Gregorio (1997) using RILs developed from a cross between IR29, a sensitive variety, and Pokkali, a highly tolerant landrace cultivated along the south-eastern coast of India and the Saltol QTL is flanked by the AFLP markers P3/M9-8 and P1/M9-3 on chromosome 1 accounting for 64.3%
80.2% of the total phenotypic variation. This QTL works largely to maintain low Na1 concentrations in plant tissue. This QTL has been further studied to a fine map and located between the SSR markers RM1287 and RM7075 between 10.71 and 15.12 Mb that encompasses the SKC1 locus (Bonilla et al., 2002; Niones, 2004; Thomson et al., 2010; Alam et al., 2011). Beyond Saltol, many QTLs have been mapped for traits like shoot Na1 concentration (SNC), shoot K1 concentration (SKC), and shoot Na1/K1 ratio by several researchers. Lin et al. (2004) detected several QTLs for Na1 and K1 uptake in shoots and roots using an F2 population derived from a cross between “Nona Bokra” and “Koshihikari,” including a major QTL responsible for SKC1 on chromosome 1. Ren et al. (2005) cloned the SKC1 QTL, which maintains K1 homeostasis in salt-tolerant varieties under salt stress and cloned the SKC1 gene which is corresponding to the OsHKT8/Os01g0307500 locus encoding a member of HKT-type transporters. Haq et al. (2010) identified a cluster of QTLs involved in leaf Na1 concentration and K1/Na1 ratio on chromosome 1 using RILs from a cross between Co 39 and

43.5 QTLS DEPLOYED IN ABIOTIC STRESS TOLERANCE IN RICE

881

Moroberekan. Koyama et al. (2001), Pandit et al. (2010), and Zheng et al. (2015) reported QTLs for the Na1 and K1 concentrations, and Na1/K1 ratio which are located in adjacent or even the same chromosomal regions as Saltol or SKC1. Similarly Bimpong et al. (2014a,b) mapped QTLs for enhancing adaptation to salinity stress using Hasawi as a salt-tolerant donor parent and SNP markers for genotyping and linkage map construction. Zhou et al. (2013) and Deng et al. (2015b) mapped QTLs qSKC-1 and qSNC-1 located between SSR markers RM283 and RM312 for SKC and SNC to the short arm of chromosome 1 using F2 mapping populations derived from salt-sensitive EMS-induced mutants of Nipponbare (rice saltsensitive rss2 and rss4) and an indica cultivar Zhaiyeqing8. Jing et al. (2017) fine mapped the qSKC-1 within a 445 kb region between the markers RM578 and IM8854 using extreme individuals from the Nipponbare/ZYQ8 and rss4/ZYQ8 F2 populations. qSKC-1 was located B3.0 Mb upstream from the SKC1 and Saltol loci. Similarly Deng et al. (2015a) used rice salt-tolerant 1 (rst1) mutant and showed that rst1 was controlled by a single recessive gene and QTL mapping between rst1 3 Peiai 64 revealed the QTL loci on chromosome 6 was to be the candidate loci of the rst1 gene. Oryza sativa early leaf senescence and salt-sensitive (OSLES) mutant produced by 60 Co γ-radiation treatment of indica cultivar Zixuan 1 was controlled by a recessive nuclear gene, and mapped in a 210 kb interval between markers IN6-005769-11/12 and RM20547 on long arm of chromosome 6 (Mao et al., 2014). Bizimana et al. (2017) identified QTLs on chromosomes 1, 2, 4, 6, 8, 9, and 12 using RILs derived from IR29 (a salt-sensitive line) and Hasawi (a salt-tolerant line) and could not find Saltol or QTLs nearby this position indicating that tolerance in Hasawi is due to novel QTLs other than Saltol/SKC1. Many QTLs linked with salt tolerance had been identified in different chromosomes of rice using bi-parental mapping populations, but fine mapping and cloning of QTLs are very limited except for the Saltol QTL region which has been inspected to a fine map, and three genes namely SKC1, SalTol and pectin esterase which are functionally characterized in this QTL region (Ren et al., 2005; Thomson et al., 2010). Ammar et al. (2009) identified QTLs controlling Na1, K1, and Cl2 ion concentrations in salt-tolerant indica rice variety CSR27 using a F2 population derived from CSR27 and MI48. Further, Pandit et al. (2010) combined QTL mapping and transcriptome profiling of extreme bulks from RIL populations derived from a CSR27 and MI48 cross which helped narrow down the QTLs into the candidate gene level (an integral transmembrane protein DUF6 and a cation chloride co-transporter). Apart from identifying genomic regions/OTLs responsible for salt stress tolerance based on biparental mapping populations, Emon et al. (2015) and Kumar et al. (2015) used association panel following a genome-wide association study approach to find marker-trait associations for salt stress tolerance. Kumar et al. (2015) identified 20 SNPs (loci) significantly associated with Na1/K1 ratio and also found the Saltol region, which is known to control salinity tolerance at seedling stage as a major association with Na1/K1 ratio at the reproductive stage. Emon et al. (2015) identified Wn11463, a STS marker for SKC1, and RM22418 on chromosome 8 as significantly associated with salinity tolerance at the seedling stage using 96 germplasm accessions with variable response to salt stress.

43.5.6 TRANSFERRING SALT STRESS TOLERANCE QTLS IN RICE FOLLOWING MAS A salt-tolerant rice line FL 478, a derivative of a IR29xPokkali cross which possesses the saltol region along with superior characteristics in comparison with the original Pokkali landrace, such as

882

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

higher salinity tolerance, photoperiod insensitivity, short stem, and early flowering has been extensively used as a donor to improve agronomically superior varieties for salt tolerance, adopting marker assisted backcross breeding with tightly linked salt-tolerant markers AP3206f, RM3412b, and RM336 for foreground selection. Several successful examples of transferring the Saltol QTL into elite rice varieties through MABC include PB1121 and PB6 (Singh et al., 2011), AS996 (Huyen et al., 2012; Cuc et al., 2015), Bac Thom 7 (Linh et al., 2012; Vu et al., 2012), Binadhan-7 (Mondal et al., 2013), BRRI Dhan-49 (Hoque et al., 2015), Rassi (Bimpong et al., 2016), and IR 64 (Ho et al., 2016).

43.5.7 IMPACT OF HEAT STRESS IN RICE CULTIVATION Rice yields are estimated to be reduced by 41% due to high-temperature stress (Ceccarelli et al., 2010). Rice yields decline by 10% for every 1 C increase in daily maximum and minimum temperature (Welch et al., 2010). Temperatures above the average daily temperature, that is, 35 C, lasting for several days causes floret sterility and abnormal pollination, thus, resulting in a lower seed setting rate (IPCC, 2007). Heat stress during panicle development inhibits the conversion of vegetative to reproductive phase and plants remain vegetative until the stress is relieved (Craufurd et al., 1993). Heat stress during flowering and anthesis can lead to failure of fertilization because of decreased pollen or ovule function which causes sterility (Matsui and Omasa, 2002). Early reproductive processes viz., micro- and megasporogenesis, pollen and stigma viability, anthesis, pollination, pollen tube growth, fertilization, and early embryo development are all highly susceptible to heat stress. Failure of any of these processes decreases the fertilization rate, or increases early embryo abortion, which reduces the number of seeds and eventually the crop yield (Young et al., 2004).

43.5.8 QTLS FOR HEAT STRESS TOLERANCE IN RICE Genetic analysis of heat tolerance revealed that heat tolerance is not controlled by a single “thermo-tolerant” gene but rather by many genes otherwise known as QTL. Different sets of genes/ QTLs have been identified which is critical for heat tolerance at different stages of the life cycle (Zhang et al., 2009; Ye et al., 2015). In rice, greater heat tolerance is required at flowering or anthesis to avoid spikelet sterility. The wild rice accessions belonging to Oryza minuta, Oryza officinalis, and Oryza glaberrima possess early morning flowering (EMF) characteristics (heat avoidance mechanism) (Sheehy et al., 2005; Prasad et al., 2006) while the EMF trait was successfully introgressed into cultivated variety Koshihikari (Ishimaru et al., 2010). Mapping of the QTLs governing heat tolerance was initiated much later compared to other abiotic stresses, with mapping of QTLs for spikelet fertility percentage using a DH population derived from an IR64 and Azucena cross which identified 6 different QTLs on 5 different chromosomes 1, 3, 4, 8, and 11 explaining a phenotypic variation from 1.28% to 19.5% (Cao et al., 2003). Later, Chang-Lan et al. (2005) used 98 backcross inbred lines derived from a cross between Nipponbare and Kasalath and mapped 3 QTLs controlling heat tolerance during grain filling on chromosome 1, 4, and 7 with a phenotypic variance of 8.94%, 17.25%, and 13.5%, respectively. Chen et al. (2008) used a RIL population derived from a cross between T219 and T226 to evaluate spikelet fertility under heat stress in natural conditions and a growth chamber and mapped QTLs for spikelet fertility on chromosome 2, 3, 8, 9, and 12 with phenotypic variation ranging from 7% to 11.4%. Zhang et al. (2008) utilized a

43.5 QTLS DEPLOYED IN ABIOTIC STRESS TOLERANCE IN RICE

883

RIL population derived from a cross between Zhongyouzao8 and Toyonishiki to map QTLs for spikelet fertility under high temperatures in green house conditions and mapped QTLs on chromosomes 2, 3, and 5 with a phenotypic variation from 6.6% to 10.7%. Zhang et al. (2009) mapped a heat tolerance QTL using 279 F2 individuals derived from a cross between 996, a heat-tolerant cultivar and 4628, a heat-sensitive cultivar. Through a combination of bulk segregant analysis and single marker analysis, a marker RM3735 on chromosome 4 and RM3586 on chromosome 3 was identified to be associated with heat tolerance, which explains 17% and 3% of the total phenotypic variation, respectively. QTLs for spikelet fertility were mapped using an F6 population consisting of 181 RILs derived from a cross between Bala, moderatelytolerant to heat stress and Azucena, a susceptible line which were grown at 30 C (control) and at high temperature (38 C) in a growth chamber. They mapped QTLs on chromosome 1, 2, 3, 8, and 10 for absolute spikelet fertility and relative spikelet fertility. The QTL on chromosome 1 for absolute spikelet fertility accounted for 17.6% of the total phenotypic variance and was consistently expressed in two controlled environmental locations while the favorable allele contributing to heat stress tolerance was from the other parent, Azucena (Jagadish et al., 2010). Xiao et al. (2011) mapped QTLs associated with seed set under high-temperature stress at the flowering stage using RILs derived from a cross between cultivar 996, a heat-tolerant genotype and cultivar 4628, a heatsusceptible genotype. The population was evaluated in the field and growth chamber conditions. They identified and mapped two QTLs for seed set percentage on chromosome 4 and 10 and they were expressed in both environmental conditions. A QTL for spikelet fertility was mapped in F2 (158 segregants) and BC1F1 (152 segregants) mapping populations involving Nagina 22 and IR64 genotypes (Ye et al., 2012). The population was grown in a net house until flowering, shifted to the growth chamber for 14 days to expose the panicle to high temperature where the day temperature was maintained at 38 C for 6 h, after which the plants were transferred back to the net house and maintained till maturity. They mapped 4 QTLs using an F2 mapping population on chromosomes 1, 4, 9, and 11 for spikelet fertility which explained 6.6%
17.6% of the total phenotypic variance and 4 SNP markers on chromosomes 1, 4, 5, and 7 for spikelet fertility in BC1F1 population involving single marker analysis were also identified. The tolerant allele of the major QTL for spikelet fertility on chromosome 4 was from Nagina 22. Poli et al. (2013) used an Ethyl methanesulphonate (EMS) induced mutant genotype of Nagina 22 for mapping QTLs in 70 F2 segregants of IR64 3 NH219 and 36 F2 segregants of its reciprocal cross. They identified markers (RM1089, RM423, RM584, and RM229) associated with the number of tillers, yield per plant, leaf senescence, and leaf width. Wei et al. (2013) mapped a major locus on chromosome 9 which confers tolerance to 48 C at the seeding stage. This major locus was mapped within an interval of 420 kb between InDel5 and RM7364 markers. Ye et al. (2015) mapped QTLs for heat tolerance at the flowering stage using two F2 populations derived from IR64 3 Giza178 (population size 86) and Milyang23 3 Giza178 (population size 96) crosses. They mapped 4 QTLs in each population on chromosomes 1, 2, 3, 4, 6, and 11 for mean spikelet fertility using a limited number of genotypes in each population. The phenotypic variation explained by each QTL varied from 12.5% to 21.8%. Also, they identified significant association markers for spikelet fertility in a three-way breeding population following TASSEL program and found markers on chromosomes 1, 2, 4, 6, and 11 associated with heat tolerance which explains a phenotypic variation ranging from 11.6% to 13.9%. Ye et al. (2015) fine mapped the QTL (qHTSF4.1) identified from an IR 64/Nagina 22 population which is responsible for spikelet fertility under high-temperature stress at the flowering stage using

884

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

BC2F2, BC3F2, BC3F3, and BC5F2 populations from the same cross and this QTL interval was narrowed down to about 1.2 Mb. The relative performance of genotypes under control and stress conditions can be used as an indicator to identify and map QTLs, which has practical relevance since genotypes with low yield potential under control conditions quite often show higher tolerance to stress than high yielding genotypes and can be further used in breeding varieties for stress tolerance (Raman et al., 2012; Kumar et al., 2015; Tiwari et al., 2016). Shanmugavadivel et al. (2017) used a RIL population derived from Nagina 22 and IR 64 and mapped 5 QTLs for heat stress tolerance using stress tolerance indices on chromosome 3, 5, 9, and 12 of which a QTL for yield and spikelet sterility indices were found to be co-localized. QTLs explained phenotypic variation in the range of 6.27%
21.29%. Of these five QTLs, two high effect QTLs, one novel (qSTIPSS9.1) and one known (qSTIY5.1/qSSIY5.2), were mapped in less than 400 Kbp genomic regions. To our knowledge, so far utilization of heat-tolerant QTLs in rice breeding has not been reported yet.

43.6 META-QTLS FOR DEVELOPING NEW VARIETIES Gramene QTL database has 835 QTLs regarding abiotic stress tolerance in rice while another database called Q-TARO (Yonemaru et al., 2010; http://qtaro.abr.affrc.go.jp/) has well documented 111 QTLs for drought, 5 for salinity, and 19 for heat tolerance in rice in addition to QTLs for other traits. This list is besides the numerous QTLs for GY and its component traits under different abiotic stress as discussed in this chapter. Together all this information is a valuable source of knowledge, but it is redundant and lacks information about consistent and precise locations of QTLs across different genetic backgrounds and environments, and therefore, is less useful in MAS. Metaanalysis of QTLs utilizes the information from previously identified QTLs from prior studies and estimates the position of meta-QTLs in a narrow genetic region with high confidence. It is a valuable statistical tool to dissect complex traits, especially epistasis and pleiotropy effects (Khowaja et al., 2009; Sandhu et al., 2017). Several studies in rice have utilized meta QTL analysis to capture meta-QTLs for GY, agronomic traits, and root characteristics under drought stress in rice plants (Khowaja et al., 2009; Swamy et al., 2011; Trijatmiko et al., 2014). Meta-analysis of QTLs supplemented with other meta-analysis tools for genomic data like RiceMetaSys (Sandhu et al., 2017) will further narrow down and identify positional as well as functional candidate genes for abiotic stress tolerance, and pave the foundation for their subsequent use in MAB programs.

43.7 CONCLUSION AND FUTURE PROSPECTS As evident from this discussion, breeding for abiotic stress tolerance using MAS has taken a quantum leap after completion of the rice genome sequencing project in 2005. The availability of a large number of uniformly distributed markers over genome made QTL mapping and its introgression into elite varieties relatively fast and precise, and, removed the bottlenecks associated with conventional breeding. A large number of QTLs for drought, heat, and salinity have been introgressed via MAS/MAB but little progress in varietal release has taken place due to dependence of performance of favorable alleles on genetic background, TPE, pleiotropism, and epistasis. The lack of robust

REFERENCES

885

screening protocols and facilities that can match field conditions has made selection of traits with low heritability difficult. Nevertheless, advances in the use of QTLs in abiotic stress are motivating and many varieties are in advance field trials stage across the globe. In future, there is a challenge to further reduce the genotyping and phenotyping cost, and to develop advanced technologies for the association of forward genetics with reverse genetics so as to identify and validate the candidate genes underlying the QTLs. The identification of small RNAs and epigenetic basis of stress tolerance will aid in the construction of a genetic model of variation for these traits and will give new targets for MAS. With the advancement of genome editing, focus will shift from QTLs to QT nucleotide, and thereby, QTL editing will play a major role in unraveling mechanisms, field performance, and target environment effects associated with abiotic stress tolerance in rice.

REFERENCES Alam, R., Sazzadur Rahman, M., Seraj, Z.I., Thomson, M.J., Ismail, A.M., Tumimbang-Raiz, E., et al., 2011. Investigation of seedling-stage salinity tolerance QTLs using backcross lines derived from Oryza sativa L. Pokkali. Plant Breed. 130 (4), 430
437. Ammar, M.H.M., Pandit, A., Singh, R.K., Sameena, S., Chauhan, M.S., Singh, A.K., et al., 2009. Mapping of QTLs controlling Na1, K1 and Cl2 ion concentrations in salt tolerant indica rice variety CSR27. J. Plant Biochem. Biotechnol. 18 (2), 139
150. Arends, D., Prins, P., Jansen, R.C., Broman, K.W., 2010. R/qtl: high-throughput multiple QTL mapping. Bioinformatics 26 (23), 2990
2992. Asch, F., Dingkuhn, M., Dorffling, K., Miezan, K., 2000. Leaf K/Na ratio predicts salinity induced yield loss in irrigated rice. Euphytica 113, 109
118. Babu, R.C., Nguyen, B.D., Chamarerk, V., Shanmugasundaram, P., Chezhian, P., Jeyaprakash, P., et al., 2003. Genetic analysis of drought resistance in rice by molecular markers. Crop Sci. 43, 1457
1459. Available from: https://doi.org/10.2135/cropsci2003.1457. Bal, A.R., Dutt, S.K., 1986. Mechanism of salt tolerance in wild rice (Oryza coarctata Roxb). Plant Soil 92, 399
404. Banerjee, S., Yandell, B.S., Yi, N., 2008. Bayesian quantitative trait loci mapping for multiple traits. Genetics 179 (4), 2275
2289. Bernier, J., Kumar, A., Venuprasad, R., Spaner, D., Atlin, G.N., 2007. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci. 47, 507
516. Bernier, J., Serraj, R., Kumar, A., Venuprasad, R., Impa, S., Gowda, R.P.V., et al., 2009. The large-effect drought-resistance QTLqtl12.1 increases water uptake in upland rice. Field Crops Res. 110 (2), 139
146. Bimpong, I.K., Manneh, B., Diop, B., Ghislain, K., Sow, A., Amoah, N.K.A., et al., 2014a. New quantitative trait loci for enhancing adaptation to salinity in rice from Hasawi, a Saudi landrace into three African cultivars at the reproductive stage. Euphytica 200 (1), 45
60. Bimpong, I.K., Manneh, B., Namaky, R., Diaw, F., Amoah, N.K.A., Sanneh, B., et al., 2014b. Mapping QTLs related to salt tolerance in rice at the young seedling stage using 384-plex single nucleotide polymorphism SNP, marker sets. Mol. Plant Breed. 5, 47
63. Bimpong, I.K., Manneh, B., Sock, M., Diaw, F., Amoah, N.K.A., Ismail, A.M., 2016. Improving salt tolerance of lowland rice cultivar ‘Rasi’ through marker-aided backcross breeding in West Africa. Plant Sci. 242, 288
299.

886

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Bizimana, J.B., Luzi-Kihupi, A., Murori, R.W., Singh, R.K., 2017. Identification of quantitative trait loci for salinity tolerance in rice (Oryza sativa L.) using IR29/Hasawi mapping population. J. Genet. 96 (4), 571
582. Blum, A., 1988. Plant Breeding for Stress Environments. CRC Press, Boca Raton, FL, USA, p. 223. Bonilla, P., Dvorak, J., Mackill, D., Deal, K., Gregorio, G., 2002. RLFP and SSLP mapping of salinity tolerance genes in chromosome 1 of rice (Oryza sativa L.) using recombinant inbred lines. Philipp Agric Sci. 85, 68
76. Botstein, D., White, R.L., Skolnick, M., Davis, R.W., 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32 (3), 314
331. Broman, K.W., Speed, T.P., 2002. A model selection approach for the identification of quantitative trait loci in experimental crosses. J. R. Stat. Soc. B 64, 641
656. Broman, K.W., Wu, H., Sen, S., Churchill, G.A., 2003. R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889
890. Cao, L., Zhao, J., Zhan, X., Li, D., He, L., Cheng, S., 2003. Mapping QTLs for heat tolerance and correlation between heat tolerance and photosynthetic rate in rice. Chin. J. Rice Sci. 17, 223
227. Catolos, M., Sandhu, N., Dixit, S., Shamsudin, N.A.A., Naredo, M.E.B., McNally, K.L., et al., 2017. Genetic loci governing grain yield and root development under variable rice cultivation conditions. Front. Plant Sci. 8. Available from: https://doi.org/10.3389/fpls.2017.01763. Ceccarelli, S., Grando, S., Maatougui, M., Michael, M., Slash, M., Haghparast, R., et al., 2010. Plant breeding and climate changes. J. Agric. Sci. 148, 627
637. Chang-Lan, Z., Ying-Hui, X., Chun-Ming, W., Ling, J., Hu-Qu, Z., Jian-Min, W., 2005. Mapping QTL for heat-tolerance at grain filling stage in rice. Rice Sci. 12 (1), 33
38. Chen, Q., Yu, S., Li, C., Mou, T., 2008. Identification of QTLs for heat tolerance at flowering stage in rice. Sci. Agric. Sin. 41, 315
321. Craufurd, P.Q., Flower, D.J., Peacock, J.M., 1993. Effect of heat and drought stress on sorghum. I. Panicle development and leaf appearance. Exp. Agric. 29, 61
76. Cuc, L.M., Quyen, P.T., Huyen, L.T.N., Ham, L.H., 2015. Breeding for the salinity tolerance rice variety in Vietnam. J. Agric. Technol. 11 (8), 2263
2272. Darvasi, A., Soller, M., 1997. A simple method to calculate resolving power and confidence interval of QTL map location. Behav. Genet. 27 (2), 125
132. Das, P., Nutan, K.K., Singla-Pareek, S.L., Pareek, A., 2015. Understanding salinity responses and adopting ‘omics-based’ approaches to generate salinity tolerant cultivars of rice. Front. Plant Sci. 6, 712. Available from: https://doi.org/10.3389/fpls.2015.00712. Deng, P., Jiang, D., Dong, Y., Shi, X., Jing, W., Zhang, W., 2015a. Physiological characterisation and fine mapping of a salt-tolerant mutant in rice. Funct. Plant Biol. 42 (11), 1026
1035. Deng, P., Shi, X., Zhou, J., Wang, F., Dong, Y., Jing, W., et al., 2015b. Identification and fine mapping of a mutation conferring salt-sensitivity in rice (Oryza sativa L.). Crop Sci. 55, 219
228. Dixit, S., Yadaw, R.B., Mishra, K.K., Kumar, A., 2017. Marker-assisted breeding to develop the droughttolerant version of Sabitri, a popular variety from Nepal. Euphytica 213, 184. Available from: https://doi. org/10.1007/s10681-017-1976-3. Emon, R.M., Islam, M.M., Halder, J., Fan, Y., 2015. Genetic diversity and association mapping for salinity tolerance in Bangladeshi rice landraces. Crop J. 3, 440
444. Falconer, D., Mackay, T., 1996. Introduction to Quantitative Genetics, fourth ed. Longmans Green, Harlow, Essex, UK. Fischer, K.S., Fukai, S., Kumar, A., Leung, H., Jongdee, B., 2012. Field phenotyping strategies and breeding for adaptation of rice to drought. Front. Physiol. 3, 1
21. Flowers, T.J., Yeo, A.R., 1981. Variability in the resistance of sodium chloride salinity within rice (Oryza sativa L.) varieties. New Phytol. 88, 363
373.

REFERENCES

887

Fukai, S., Cooper, M., 1995. Development of drought-resistant cultivars using physio morphological traits in rice. Field Crops Res. 40, 67
86. Geldermann, H., 1975. Investigations on inheritance of quantitative characters in animals by gene markers\rI. Methods. Theor. Appl. Genet. 46, 319
330. Ghimire, K.H., Quiatchon, L.A., Vikram, P., Mallikarjuna, S.B.P., Dixit, S., Ahmed, H., et al., 2012. Identification and mapping of a QTL (qDTY 1.1) with a consistent effect on grain yield under drought. Field Crops Res. 131, 88
96. Gregorio, G.B., 1997. Tagging Salinity Tolerance Genes in Rice Using Amplified Fragment Length Polymorphism (AFLP), (PhD. Thesis). University of the Philippines, Los Ban˜os, p. 118. Gusella, J.F., Wexler, N.S., Conneally, P.M., Naylor, S.L., Anderson, M.A., Tanzi, R.E., et al., 1983. A polymorphic DNA marker genetically linked to Huntington’s disease. Nature 306 (5940), 234
238. Hakim, M.A., Juraimi, A.S., Begum, M., Hanafi, M.M., Ismail, M.R., Selamat, A., 2010. Effect of salt stress on germination and early seedling growth of rice (Oryza sativa L.). Afr. J. Biotechnol. 9, 1911
1918. Haley, C.S., Knott, S.A., 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69 (4), 315. Haq, T.U., Gorham, J., Akhtar, J., Akhtar, N., Steele, K.A., 2010. Dynamic quantitative trait loci for salt stress components on chromosome 1 of rice. Funcl. Plant Biol. 37, 634
645. Hern´andez Mora, J.R., Micheletti, D., Bink, M., et al., 2017. Integrated QTL detection for key breeding traits in multiple peach progenies. BMC Genomics 18, 404. Available from: https://doi.org/10.1186/s12864-0173783-6. Ho, V.T., Thomson, M.T., Ismail, A.M., 2016. Development of salt tolerant IR64 near isogenic lines through marker-assisted breeding. J. Crop Sci. Biotechnol. 19 (5), 373
381. Hoang, T.M.L., Tran, T.N., Nguyen, T.K.T., Williams, B., Wurm, P., Bellairs, S., et al., 2016. Improvement of salinity stress tolerance in rice: challenges and opportunities. Agronomy 6, 54. Available from: https://doi. org/10.3390/agronomy6040054. Hoque, A.B.M.Z., Haque, M.A., Sarker, M.R.A., Rahman, M.A., 2015. Marker-assisted introgression of saltol locus into genetic background of BRRI Dhan-49. Int. J. Biosci. 6, 71
80. Huang, A., Xu, S., Cai, X., 2015. Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity (Edinb) 114, 107
115. Huyen, N.T.L., Cuc, M.L., Ismail, A.M., Ham, H.L., 2012. Introgression the salinity tolerance QTLs saltol into AS996, the elite rice variety of Vietnam. Am. J. Plant Sci. 3, 981
987. IPCC, 2007. Summary for Policymakers. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 1
18. Ishimaru, T., Hirabayashi, H., Ida, M., Takai, T., San-Oh, Y.A., Yoshinaga, S., et al., 2010. A genetic resource for early-morning flowering trait of wild rice Oryza officinalis to mitigate high temperature-induced spikelet sterility at anthesis. Ann. Bot. 106, 515
520. Jagadish, S., Cairns, J., Lafitte, R., Wheeler, T., Price, A., Craufurd, P., 2010. Genetic analysis of heat tolerance at anthesis in rice. Crop Sci. 50, 1633
1641. Jansen, R.C., Stam, P., 1994. High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136, 1447
1455. Jansen, R.C., Nap, J.P., 2001. Genetical genomics: the added value from segregation. Trends Genet. 17, 388
391. Jing, W., Deng, P., Cao, C., Zhang, W., 2017. Fine mapping of qSKC-1, a major quantitative trait locus for shoot K 1 concentration, in rice seedlings grown under salt stress. Breed Sci. 67 (3), 286
295.

888

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Joehanes, R., Nelson, J.C., 2008. QGene 4.0, an extensible Java QTL-analysis platform. Bioinformatics 24, 2788
2789. Kalendar, R., Schulman, A.H., 2006. IRAP and REMAP for retrotransposon-based genotyping and fingerprinting. Nat. Protoc. 1 (5), 2478
2484. Kao, C.H., Zeng, Z.B., Teasdale, R.D., 1999. Multiple interval mapping for quantitative trait loci. Genetics 152, 1203
1216. Kearsey, M., 1998. The principles of QTL analysis (a minimal mathematics approach). J. Exp. Bot. 49, 1619
1623. Khan, A.M., 2012. Current status of genomic based approaches to enhance drought tolerance in rice (Oryza sativa L.), an overview. Mol. Plant Breed. 3 (1), 1
10. Khowaja, F.S., Norton, G.J., Courtois, B., Price, A.H., 2009. Improved resolution in the position of droughtrelated QTLs in a single mapping population of rice by meta-analysis. BMC Genomics 10 (1), 276. Available from: https://doi.org/10.1186/1471-2164-10-276. Koyama, M.L., Levesley, A., Koebner, R.M., Flowers, T.J., Yeo, A.R., 2001. Quantitative trait loci for component physiological traits determining salt tolerance in rice. Plant Physiol. 125 (1), 406
422. 2001. Kumar, A., Bernier, J., Verulkar, S., Lafitte, H.R., Atlin, G.N., 2008. Breeding for drought tolerance: Direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations. Field Crops Res. 107, 221
231. Kumar, V., Singh, A., Mithra, S.V.A., Krishnamurthy, S.L., Parida, S.K., Jain, S., et al., 2015. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res 22 (2), 133
145. Available from: https://doi.org/10.1093/dnares/dsu046. Kumasaka, N., Knights, A.J., Gaffney, D.J., 2015. Fine-mapping cellular QTLs with RASQUAL and ATACseq. Nat. Genet. 48, 206
213. Lanceras, J.C., Pantuwan, G., Jongdee, B., Toojinda, T., 2004. Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiol. 135, 384
399. Lander, E.S., Botstein, D., 1986. Strategies for studying heterogeneous genetic traits in humans by using a linkage map of restriction fragment length polymorphisms. Proc. Natl. Acad. Sci. U.S.A. 83 (19), 7353
7357. Lander, E.S., Botstein, D., 1987. Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science (New York, NY) 236, 1567
1570. Lander, E.S., Botstein, D., 1989. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121 (1), 185
199. Lee, K.S., et al., 2003. Salinity tolerance of japonica and indica rice (Oryza sativa L.) at the seedling stage. Planta 216 (6), 1043
1046. Li, G., Quiros, C.F., 2001. Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor. Appl. Genet. 103 (2
3), 455
461. Li, S., Wang, J., Zhang, L., 2015. Inclusive composite interval mapping of QTL by environment interactions in biparental populations. PLoS ONE 10 (7), e0132414. Available from: https://doi.org/10.1371/journal. pone.0132414. Li, H., Ye, G., Wang, J., 2007. A modified algorithm for the improvement of composite interval mapping. Genetics 175, 361
374. Lin, H.X., Zhu, M.Z., Yano, M., Gao, J.P., Liang, Z.W., Su, W.A., et al., 2004. QTLs for Na 1 and K 1 uptake of the shoots and roots controlling rice salt tolerance. Theor. Appl. Genet. 108 (2), 253
260. Linh, H.L., Linh, H.T., Xuan, D.T., Ham, H.L., Ismail, A.M., Khanh, D.T., 2012. Molecular breeding to improve salt tolerance of rice (Oryza sativa L.) in the red river delta of Vietnam. Int. J. Plant Genom 2012. Available from: https://doi.org/10.1155/2012/949038.

REFERENCES

889

Luria, S.E., Human, M.L., 1952. A nonhereditary, host-induced variation of bacterial viruses. J. Bacteriol. 64, 557
569. Lutts, S., Kinet, J.M., Bouharmont, J., 1995. Changes in plant response to NaCl during development of rice (Oryza sativa L.) varieties differing in salinity resistance. J. Exp. Bot. 46, 1843
1852. Lynch, M., Walsh, B., 1998. Genetics and Analysis of Quantitative Traits, vol. 1. Sinauer Associates, pp. 535
557. Mackay, T.F., 2001. The genetic architecture of quantitative traits. Annu. Rev. Genet. 35 (1), 303
339. Manly, K.F., Cudmore Jr, R.H., Meer, J.M., 2001. Map Manager QTX, cross-platform software for genetic mapping. Mamm. Genome 12 (12), 930
932. Mansfeld, B.N., Grumet, R., 2018. QTLseqr: an R package for bulk segregant analysis with next-generation sequencing. Plant Genome 11. Available from: https://doi.org/10.1101/208140bioRxiv, 208140. Mao, J.J., Zhao, C., Huang, F.G., Pan, G., Cheng, F.M., 2014. Physiological characterization and gene fine mapping of a leaf early senescence and salt-sensitive mutant osles in rice. Acta Agron. Sin. 40 (5), 769. Available from: https://doi.org/10.3724/SP.J.1006.2014.00769. Martinville, D.B., Wyman, A.R., White, R., Francke, U., 1982. Assignment of first random restriction fragment length polymorphism (RFLP) locus ((D14S1) to a region of human chromosome 14. Am. J. Hum. Genet. 34 (2), 216
226. Matsui, T., Omasa, K., 2002. Rice cultivars tolerant to high temperature at flowering anther characteristics. Ann. Bot. 89, 683
687. Mills, R.E., Luttig, C.T., Larkins, C.E., Beauchamp, A., Tsui, C., Pittard, W.S., et al., 2006. An initial map of insertion and deletion (INDEL) variation in the human genome. Genome Res. 16 (9), 1182
1190. Mishra, K.K., Vikram, P., Yadaw, R.B., Swamy, B.M., Dixit, S., Cruz, M.T.S., et al., 2013. qDTY 12.1: a locus with a consistent effect on grain yield under drought in rice. BMC Genet. 14 (1), 12. Available from: https://doi.org/10.1186/1471-2156-14-12. Mohammadi-Nejada, G., Singh, R.K., Arzanic, A., Rezaiec, A.M., Sabourid, H., Gregorio, G.B., 2010. Evaluation of salinity tolerance in rice genotypes. Int. J. Plant Prod. 4, 199
207. Mondal, U., Khanom, M.S.R., Hassan, L., Begum, S.N., 2013. Foreground selection through SSRs markers for the development of salt tolerant rice variety. J. Bangladesh Agric. Univ. 11, 67
72. Available from: https://doi.org/10.3329/jbau.v11i1.18215. Moradi, F., Ismail, A.M., 2007. Responses of photosynthesis, chlorophyll fluorescence and ROS-scavenging systems to salt stress during seedling and reproductive stages in rice. Ann. Bot. 99, 1161
1173. Moradi, F., Ismail, A.M., Gregorio, G., Egdane, J., 2003. Salinity tolerance of rice during reproductive development and association with tolerance at seedling stage. Indian J. Plant Physiol. 8, 105
116. Munns, R., 2005. Genes and salt tolerance: bringing them together. New Phytol. 167, 645
663. Munns, R., Tester, M., 2008. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 59, 651
681. Niones, J.M., 2004. Five mapping of the salinity tolerance gene on chromosome 1 of rice (Oryza sativa L.) using near-isogenic lines. MS Dissertation, University of the Philippines, Los Ban˜os, Laguna. Olson, M., Hood, L., Cantor, C., Botstein, D., 1989. A common language for physical mapping of the human genome. Science 245 (4925), 1434
1435. Ongen, H., Buil, A., Brown, A.A., et al., 2016. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479
1485. Orita, M., Iwahana, H., Kanazawa, H., Hayashi, K., Sekiya, T., 1989. Detection of polymorphisms of human DNA by gel electrophoresis as single-strand conformation polymorphisms. Proc. Natl. Acad. Sci. U.S.A. 86 (8), 2766
2770. Pandit, A., Rai, V., Bal, S., Sinha, S., Kumar, V., Chauhan, M., et al., 2010. Combining QTL mapping and transcriptome profiling of bulked RILs for identification of functional polymorphism for salt tolerance genes in rice (Oryza sativa L.). Mol. Genet. Genomics 284 (2), 121
136.

890

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Pantuwan, G., Fukai, S., Cooper, M., Rajatasereekul, S., O’toole, J.C., 2002. Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands: 2. Selection of drought resistant genotypes. Field Crops Res. 73 (2), 169
180. Poli, Y., Basava, R.K., Panigrahy, M., Vinukonda, V.P., Dokula, N.R., Voleti, S.R., et al., 2013. Characterization of a Nagina22 rice mutant for heat tolerance and mapping for yield traits. Rice 6, 36. Prasad, P.V.V., Boote, K.J., Allen Jr, L.H., Sheehy, J.E., Thomas, J.M.G., 2006. Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crops Res. 95, 398
411. Price, A.H., Cairns, J.E., Horton, P., Jones, H.G., Griffiths, H., 2002a. Linking drought-resistance mechanisms to drought avoidance in upland rice using a QTL approach: progress and new opportunities to integrate stomatal and mesophyll responses. J. Exp. Bot. 53 (371), 989
1004. Price, A.H., Steele, K.A., Moore, B.J., Jones, R.G.W., 2002b. Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: II. Mapping quantitative trait loci for root morphology and distribution. Field Crops Res. 76 (1), 25
43. Raman, A., Verulkar, S., Mandal, N., Variar, M., Shukla, V., Dwivedi, J., et al., 2012. Drought yield index to select high yielding rice lines under different drought stress severities. Rice 5 (1), 31. Available from: https://doi.org/10.1186/1939-8433-5-31. Ren, Z.H., Gao, J.P., Li, L.G., Cai, X.L., Huang, W., Chao, D.Y., et al., 2005. A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat. Genet. 37 (10), 1141
1146. Rosielle, A.A., Hamblin, J., 1981. Theoretical aspects of selection for yield in stress and non-stress environment. Crop Sci. 21, 943
946. Sandhu, N., Singh, A., Dixit, S., Cruz, M.T.S., Maturan, P.C., Jain, R.K., et al., 2014. Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet. 15 (1), 63. Available from: https://doi.org/10.1186/1471-2156-15-63. Sandhu, N., Torres, R., Sta Cruz, M.T., Maturan, P.C., Jain, R., Kumar, A., et al., 2015. Traits and QTLs for development of dry direct seeded rainfed rice varieties. J. Exp. Bot. 66, 225
244. Sandhu, M., Sureshkumar, V., Prakash, C., Dixit, R., Solanke, A.U., Sharma, T.R., et al., 2017. RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement. BMC Bioinformatics 18 (1), 432. Available from: https://doi.org/10.1186/s12859-017-1846-y. Satagopan, J.M., Yandell, B.S., Newton, M.A., Osborn, T.C., 1996. A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics 144 (2), 805
816. Sax, K., 1923. The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8 (6), 552
560. Seaton, G., Haley, C.S., Knott, S.A., et al., 2002. QTL express: mapping quantitative trait loci in simple and complex pedigrees. Bioinformatics 18, 339
340. Shamsudin, N.A.A., Swamy, B.P.M., Ratnam, W., Sta. Cruz, M.T., Sandhu, N., Raman, A.K., et al., 2016. Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 9, 21. Available from: https://doi.org/10.1186/s12284-016-0093-6. Shanmugavadivel, P.S., Mithra, S.V.A., Prakash, C., et al., 2017. High resolution mapping of QTLs for heat tolerance in rice using a 5K SNP array. Rice 10, 28. Available from: https://doi.org/10.1186/s12284-0170167-0. Sheehy, J., Elmido, A., Centeno, G., Pablico, P., 2005. Searching for new plants for climate change. J. Agric. Met. 60, 463
468. Sillanpa¨a¨, M.J., Corander, J., 2002. Model choice in gene mapping: what and why. Trends Genet. 18, 302
307. Singh, R.K., Gopalakrishnan, S., Singh, V.P., Prabhu, K.V., Mohapatra, T., Singh, N.K., et al., 2011. Marker assisted selection: a paradigm shift in basmati breeding. Indian J. Genet. 71, 120
128.

REFERENCES

891

Singh, R., Singh, Y., Xalaxo, S., Verulkar, S., Yadav, N., Singh, S., et al., 2016. From QTL to varietyharnessing the benefits of QTLs for drought, flood and salt tolerance in mega rice varieties of India through a multi-institutional network. Plant Sci. 242, 278
287. Soller, M., Brody, T., Genizi, A., 1976. On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47 (1), 35
39. Sun, L., Wang, J., Zhu, X., et al., 2017. HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly. Brief Bioinform. Available from: https://doi.org/10.1093/bib/bbx011. Swamy, B.M., Vikram, P., Dixit, S., Ahmed, H.U., Kumar, A., 2011. Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genomics 12 (1), 319. Available from: https://doi.org/10.1186/1471-2164-12-319. Thitisaksakul, M., Tananuwong, K., Shoemaker, C.F., Chun, A., Tanadul, O., Labavitch, J.M., et al., 2015. Effects of timing and severity of salinity stress on rice (Oryza sativa L.) yield, grain composition, and starch functionality. J. Agric. Food Chem. 63 (8), 2296
2304. Available from: https://doi.org/10.1021/ jf503948p. Thoday, J.M., 1961. Location of polygenes. Nature 191, 368
370. Thomson, M.J., de Ocampo, M., Egdane, J., Rahman, M.K., Sajise, A.G., Adorada, D.L., et al., 2010. Characterizing the saltol quantitative trait locus for salinity tolerance in rice. Rice 3, 148
160. Available from: https://doi.org/10.1007/s12284-010-9053-8. Tiwari, S., Krishnamurthy, S.L., Kumar, V., Singh, B., Rao, A.R., Mithra, S.V.A., et al., 2016. Mapping QTLs for salt tolerance in rice (Oryza sativa L.) by bulked segregant analysis of recombinant inbred lines using 50K SNP chip. PLoS ONE 11 (4), e0153610. Available from: https://doi.org/10.1371/journal. pone.0153610. Trijatmiko, K.R., Supriyanta, Prasetiyono, J., Thomson, M.J., Vera Cruz, C.M., Moeljopawiro, S., et al., 2014. Meta-analysis of quantitative trait loci for grain yield and component traits under reproductive-stage drought stress in an upland rice population. Mol. Breed. 34, 283
295. Uga, Y., Okuno, K., Yano, M., 2011. Dro1, a major QTL involved in deep rooting of rice under upland field conditions. J. Exp. Bot. 62, 2485
2494. Uga, Y., Sugimoto, K., Ogawa, S., Rane, J., Ishitani, M., Hara, N., et al., 2013. Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat. Genet. 45, 1097
1102. Venuprasad, R., Lafitte, H.R., Atlin, G.N., 2007. Response to direct selection for grain yield under drought stress in rice. Crop Sci. 47, 285
293. Available from: https://doi.org/10.2135/cropsci2006.03.0181. Venuprasad, R., Dalid, C.O., Del Valle, M., Zhao, D., Espiritu, M., Sta Cruz, M.T., et al., 2009. Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor. Appl. Genet. 120, 177
190. Venuprasad, R., Bool, M.E., Quiatchon, L., Cruz, M.S., Amante, M., Atlin, G.N., 2012a. A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol. Breed. 30 (1), 535
547. Venuprasad, R., Bool, M.E., Quiatchon, L., Atlin, G.N., 2012b. A QTL for rice grain yield in aerobic environments with large effects in three genetic backgrounds. Theor. Appl. Genet. 124 (2), 323
332. Vikram, P., Swamy, B.P.M., Dixit, S., Ahmed, H.U., Teresa, M., Cruz, S., 2011. Reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet. 12, 1
15. Vikram, P., Swamy, B.M., Dixit, S., Trinidad, J., Cruz, M.T.S., Maturan, P.C., et al., 2016. Linkages and interactions analysis of major effect drought grain yield QTLs in rice. PLoS ONE 11. Available from: https:// doi.org/10.1371/journal.pone.0151532. Vos, P., Hogers, R., Bleeker, M., Reijans, M., Lee, T.V.D., Hornes, M., et al., 1995. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 23 (21), 4407
4414.

892

CHAPTER 43 DEVELOPING ABIOTIC STRESS TOLERANCE IN RICE

Vu, H.T.T., Le, D.D., Ismail, A.M., Le, H.H., 2012. Marker-assisted backcrossing (MABC) for improved salinity tolerance in rice (Oryza sativa L.) to cope with climate change in Vietnam. Aust. J. Crop Sci. 6, 1649
1654. Wang, D.G., Fan, J.B., Siao, C.J., Berno, A., Young, P., Sapolsky, R., et al., 1998. Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280 (5366), 1077
1082. Wang, S. et al., 2006. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. hhttp://statgen.ncsu.edu/qtlcart/WQTLCart.htmi. Wei, H., Liu, J., Wang, Y., Huang, N., Zhang, X., Wang, L., et al., 2013. A dominant major locus in chromosome 9 of rice (Oryza sativa L.) confers tolerance to 48 C high temperature at seeding stage. J. Hered. 104, 287
294. Welch, J.R., Vincent, J.R., Auffhammer, M., Moya, P.F., Dobermann, A., Dawe, D., 2010. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc. Natl. Acad. Sci. U.S.A. 107, 14562
14567. World Water Development Report, 2012. hhttp://www.unesco.org/new/en/natural-ciences/environment/water/ wwap/wwdr/wwdr4-2012/i. Xangsayasane, P., Jongdee, B., Pantuwan, G., Fukai, S., Mitchell, J.H., 2014. Genotypic performance under intermittent and terminal drought screening in rainfed lowland rice. Field Crops Res. 156, 281
292. Xiao, Y.H., Pan, Y., Luo, L.H., Deng, H.B., Zhang, G.L., Tang, W.B., et al., 2011. Quantitative trait loci associated with pollen fertility under high temperature stress at flowering stage in rice (Oryza sativa). Rice Sci. 18 (2), 1
7. Yadaw, R.B., Dixit, S., Raman, A., Mishra, K.K., Vikram, P., Swamy, B.P.M., et al., 2013. A QTL for high grain yield under lowland drought in the background of popular rice variety Sabitri from Nepal. Field Crops Res. 144, 281
287. Yamane, K., Kawasaki, M., Taniguchi, M., Miyake, H., 2008. Correlation between chloroplast ultrastructure and chlorophyll fluorescence characteristics in the leaves of rice (Oryza sativa L.) grown under salinity. Plant Prod. Sci. 11, 139
145. Yandell, B.S., Mehta, T., Banerjee, S., et al., 2007. R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses. Bioinformatics 23, 641
643. Available from: https://doi.org/10.1093/bioinformatics/ btm011. Yang, J., Hu, C., Hu, H., et al., 2008. QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24, 721
723. Available from: https://doi.org/10.1093/ bioinformatics/btm494. Yano, K., Takashi, T., Nagamatsu, S., Kojima, M., Sakakibara, H., Kitano, H., et al., 2012. Efficacy of microarray profiling data combined with QTL mapping for the identification of a QTL gene controlling the initial growth rate in rice. Plant Cell Physiol. 53 (4), 729
739. Ye, C., Argayoso, M.A., Redon˜a, E.D., Sierra, S.L., Laza, M.A., Dilla, C.J., et al., 2012a. Mapping QTL for heat tolerance at flowering stage in rice using SNP markers. Plant Breed. 131 (1), 33
41. Ye, C., Tenorio, F.A., Argayoso, M.A., Laza, M.A., Koh, H.J., Redon˜a, E.D., et al., 2015. Identifying and confirming quantitative trait loci associated with heat tolerance at flowering stage in different rice populations. BMC Genet. 16, 41. Available from: https://doi.org/10.1186/s12863-015-0199-7. Yonemaru, J.I., Yamamoto, T., Fukuoka, S., Uga, Y., Hori, K., Yano, M., 2010. QTARO: QTL annotation rice online database. Rice 3, 194
203. Young, L.W., Wilen, R.W., Bonham-Smith, P.C., 2004. High temperature stress of Brassica napus during flowering reduces micro-and megagametophyte fertility, induces fruit abortion, and disrupts seed production. J. Exp. Bot. 55 (396), 485
495. Zeng, Z.B., 1994. Precision mapping of quantitative trait loci. Genetics 136, 1457
1468.

FURTHER READING

893

Zhang, T., Yang, L., Jiang, K., Huang, M., Sun, Q., Chen, W., et al., 2008. QTL mapping for heat tolerance of the tassel period of rice. Mol. Plant Breed. 6, 867
873. Zhang, G., Chen, L., Xiao, G., Xiao, Y., Chen, X., Zhang, S., 2009. Bulked segregant analysis to detect QTL related to heat tolerance in rice using SSR markers. Agric. Sci. China 8, 482
487. Zheng, H., Zhao, H., Liu, H., Wang, J., Zou, D., 2015. QTL analysis of Na 1 and K 1 concentrations in shoots and roots under NaCl stress based on linkage and association analysis in japonica rice. Euphytica 201, 109
121. Zhou, J., Wang, F., Deng, P., Jing, W., Zhang, W., 2013. Characterization and mapping of a salt-sensitive mutant in rice (Oryza sativa L.). J. Integr. Plant Biol. 55 (6), 504
513. Ziyatdinov, A., Brunel, H., Martinez-Perez, A., et al., 2016. Solarius: an R interface to SOLAR for variance component analysis in pedigrees. Bioinformatics 32, 1901
1902. Available from: https://doi.org/10.1093/ bioinformatics/btw080. Ziyatdinov, A., Vazquez-Santiago, M., Brunel, H., et al., 2018. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics 19 (1), 68. Available from: https://doi.org/10.1186/s12859-018-2057-x.

FURTHER READING Dixit, S., Swamy, B.M., Vikram, P., Ahmed, H., Cruz, M.S., Amante, M., et al., 2012. Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities. Theor. Appl. Genet. 125, 155
169. Joseph, B., Jini, D., 2010. Proteomic analysis of salinity stress-responsive proteins in plants. Asian J. Plant Sci. 9, 307
313. Kumar, R., Venuprasad, R., Atlin, G.N., 2007. Genetic analysis of rainfed lowland rice drought tolerance under naturally-occurring stress in eastern India: heritability and QTL effects. Field Crops Res. 103, 42
52.